Data Engineer

Job Description:
Our customer is hiring a Data Engineer to join a highly visible team that sits in the middle of everything—owning the data and infrastructure that supports 70,000+ internal users across the business. This team plays a key role in keeping data clean, organized, and usable across internal systems. This person will build and enhance data pipelines within a modern cloud data environment, with opportunities to get into new Greenfield work. It’s a high-impact role where you’ll work across multiple systems, partner closely with engineering teams, and have a real hand in how data is structured.

Requirements:
• 5+ years of experience in Data Engineering, supporting enterprise-scale data environments across multiple systems and data sources
• Expert-level SQL skills (PostgreSQL preferred, some Microsoft SQL Server), writing complex queries, joins, stored procedures, and views to manipulate and transform large datasets
• Python experience, building and maintaining ETL pipelines to extract, transform, and load data from multiple enterprise sources
• AWS Redshift experience, working within a cloud data warehouse to store, optimize, and deliver data for reporting and downstream applications
• Experience with relational databases (MySQL, PostgreSQL, SQL Server), managing and querying structured data across multiple systems

Nice to have:
• Databricks experience to support scaling data processing as the platform grows
• Experience with orchestration tools (e.g., Rundeck, Lambda) to support scheduling and automation of data workflows
• NoSQL experience (MongoDB, DocumentDB, CosmosDB) to support future-state data architecture

Responsibilities:
• Clean up and tighten key datasets to support final migration efforts, ensuring data is accurate and usable across systems
• Write and optimize complex SQL queries, stored procedures, and data structures to support business-critical needs
• Build and maintain ETL pipelines using Python to move and transform data across multiple systems
• Work within AWS Redshift to manage and optimize how data is stored and accessed
• Partner with engineering teams to understand data needs and ensure data is structured in a way that supports the business
• Support ongoing improvements to the data environment as the team moves beyond migration into more scalable, long-term solutions